The set of \(N \times N_s \times N_t\)  superpixel regions is used to train a ConvNet with a standard architecture for binary image classification. We use 5 cascaded layers of convolutional filters to compute image features. Other layers of the ConvNet perform max-pooling operations or consist of fully-connected neural networks. Our ConvNet ends with a final 2-way softmax layer for `pancreas' and `non-pancreas' classification (see Figure REF ). The fully connected layers are constrained in order to avoid overfitting. We use “DropOut” for this purpose. DropOut is a method that behaves as a regularizer when training the ConvNet [1]}, [2]}. GPU acceleration allows efficient training of the ConvNet. We use an open-source implementation (cuda-convnet2https://code.google.com/p/cuda-convnet2) by Krizhevsky et al. [3]}, [4]} which efficiently trains the ConvNet, using GPU acceleration. Further speed-ups are achieved using rectified linear units as neuron activation function instead of the traditional neuron model \(f(x) = \tanh (x)\)  or \(f(x) = (1 + e^{-x})^{-1}\)  in both training and testing [3]}.
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